Kütle Spektrometresi Verileri Kullanılarak Yumurtalık Kanserinin Yapay Sinir Ağlarıyla Sınıflandırılması

Year 2021, , 781 - 790, 30.09.2021

Demet Yeşilbaş , Ayşegül Güven

https://doi.org/10.21605/cukurovaumfd.1005791

Cited By: 1

Abstract

Kanserin fark edilme aşaması, diğer kanser türlerinde olduğu gibi iyileşme oranını etkiler. Yaşı ilerlemiş kadınlar için ciddi bir hastalık olan yumurtalık kanseri başlangıç aşamasında fark edilmez, çoğu zaman diğer hastalıklarla karıştırılır. Yüzey Güçlendirmeli Lazer Desorpsiyon/İyonizasyon Uçuş Zamanlı Kütle Spektrometresi (SELDI-TOF-MS) kompleks numunelerin incelenmesine olanak sağlayarak yumurtalık ve diğer kanser türlerinin ayırt edilmesinde potansiyel belirteçtir. Bu çalışmada, FDA-NCI web sitesinde yer alan yumurtalıklara ait iki Düşük Çözünürlüklü SELDI-TOF-MS veri setini Yapay Sinir Ağları (YSA) ile sınıflandırarak, veri setlerini karşılaştırıyoruz. Ön işleme adımı olarak, Temel Bileşenler Analizi (PCAPrincipal Component Analysis) kullandık. PCA uygulanmış verinin en yüksek varyans oranına sahip 20 bileşeni seçildi, 10 nörondan oluşan tek gizli katmanlı İleri Yönlü YSA ile sınıflandırma yapıldı ve 4-3-02
isimli veri seti için %95 doğruluk elde edilirken, 8-7-02 isimli veri seti için %100 doğruluk elde edilmiştir

Keywords

Yapay sinir ağları, Temel bileşenler analizi, Yumurtalık kanseri, SELDI-TOF-MS

Classification of Ovarian Cancer with Neural Networks Using Mass Spectrometry Data

Abstract

The stage of cancer diagnosis affects the rate of recovery, as in other types of cancer. Ovarian cancer is a serious disease for older women, is not noticed at the initial stage and is often confused with other diseases. Surface-Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (SELDITOF-MS) is a potential biomarker for distinguishing ovarian and other types of cancer by allowing the examination of complex samples. In this study, we classified two Low-Resolution SELDI-TOF-MS ovarian datasets from the FDA-NCI website with Artificial Neural Networks (ANN) and compared them. We used Principal Component Analysis (PCA) as a preprocessing step of classification. 20 components of maximum variance in the PCA-applied data are selected and classified with the feed-forward ANN consists of a single hidden layer with 10 neurons, 95% accuracy was achieved for the data set named 4-3- 02 and 100% accuracy achieved for the data set named 8-7-02.

Keywords

Artificial neural networks, Principal components analysis, Ovarian cancer, SELDI-TOF-MS

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